list_evidence_packs
Retrieve and browse saved evidence packs for academic literature management. View organized collections of research materials with pagination controls.
Instructions
列出所有证据包
查看已保存的证据包列表。
Args: limit: 返回数量限制，默认 20 offset: 分页偏移量，默认 0
Returns: 证据包列表
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No |
Implementation Reference
- The core handler function for the 'list_evidence_packs' tool. It uses a SQL query to fetch paginated list of evidence packs from the database, including counts of items and documents per pack, and returns structured JSON with total count and pack summaries.@mcp.tool() def list_evidence_packs(limit: int = 20, offset: int = 0) -> dict[str, Any]: """列出所有证据包 查看已保存的证据包列表。 Args: limit: 返回数量限制，默认 20 offset: 分页偏移量，默认 0 Returns: 证据包列表 """ try: packs = query_all( """ SELECT ep.pack_id, ep.query, ep.created_at::text, COUNT(epi.id) as item_count, COUNT(DISTINCT epi.doc_id) as doc_count FROM evidence_packs ep LEFT JOIN evidence_pack_items epi ON ep.pack_id = epi.pack_id GROUP BY ep.pack_id ORDER BY ep.created_at DESC LIMIT %s OFFSET %s """, (limit, offset) ) total = query_one("SELECT COUNT(*) as count FROM evidence_packs") return { "total": total["count"] if total else 0, "limit": limit, "offset": offset, "packs": [ { "pack_id": p["pack_id"], "query": p["query"], "created_at": p["created_at"], "item_count": p["item_count"], "doc_count": p["doc_count"], } for p in packs ], } except Exception as e: return { "error": str(e), "total": 0, "packs": [], }
- src/paperlib_mcp/server.py:16-37 (registration)The registration of writing tools (including list_evidence_packs) via import and call to register_writing_tools(mcp) on the FastMCP server instance.from paperlib_mcp.tools.writing import register_writing_tools # M2 GraphRAG 工具 from paperlib_mcp.tools.graph_extract import register_graph_extract_tools from paperlib_mcp.tools.graph_canonicalize import register_graph_canonicalize_tools from paperlib_mcp.tools.graph_community import register_graph_community_tools from paperlib_mcp.tools.graph_summarize import register_graph_summarize_tools from paperlib_mcp.tools.graph_maintenance import register_graph_maintenance_tools # M3 Review 工具 from paperlib_mcp.tools.review import register_review_tools # M4 Canonicalization & Grouping 工具 from paperlib_mcp.tools.graph_relation_canonicalize import register_graph_relation_canonicalize_tools from paperlib_mcp.tools.graph_claim_grouping import register_graph_claim_grouping_tools from paperlib_mcp.tools.graph_v12 import register_graph_v12_tools register_health_tools(mcp) register_import_tools(mcp) register_search_tools(mcp) register_fetch_tools(mcp) register_writing_tools(mcp)
- Supporting helper function get_evidence_pack used in other writing tools, but referenced in the context of evidence packs.def get_evidence_pack(pack_id: int) -> EvidencePack | None: """获取证据包内容 Args: pack_id: 证据包 ID Returns: 证据包对象，如果不存在返回 None """ # 获取证据包元数据 pack = query_one( """ SELECT pack_id, query, params_json, created_at::text FROM evidence_packs WHERE pack_id = %s """, (pack_id,) ) if not pack: return None # 获取证据包条目 items = query_all( """ SELECT epi.doc_id, epi.chunk_id, epi.rank, c.page_start, c.page_end, c.text FROM evidence_pack_items epi JOIN chunks c ON epi.chunk_id = c.chunk_id WHERE epi.pack_id = %s ORDER BY epi.rank """, (pack_id,) ) # 统计 unique_docs = len(set(item["doc_id"] for item in items)) return EvidencePack( pack_id=pack["pack_id"], query=pack["query"], params=pack["params_json"] or {}, items=[ EvidencePackItem( doc_id=item["doc_id"], chunk_id=item["chunk_id"], page_start=item["page_start"], page_end=item["page_end"], text=item["text"], score=1.0 / (item["rank"] + 1) if item["rank"] is not None else 0.5, # 基于排名的伪分数 ) for item in items ], stats={ "total_chunks": len(items), "unique_docs": unique_docs, } )
- Pydantic model for EvidencePack structure, used in related tools for type safety and serialization.class EvidencePack(BaseModel): """证据包""" pack_id: int query: str params: dict[str, Any] items: list[EvidencePackItem] stats: dict[str, Any]